Aircraft navigation has the potential of being greatly improved by the computerized rendering of terrain for a pilot. This technology, sometimes known as synthetic vision, accesses a database having information relevant to the terrain desired to be displayed. A pilot viewing a terrain rendering based upon the database can therefore view nearby terrain and obstacles even in poor visibility conditions.
The creation of terrain databases, useful for avionics applications, has typically depended upon one of two general types of technology, each with different types of errors associated therewith. To ensure the terrain database is accurate, it is important to be able to discover errors in the terrain data. The first technology is photogrammetry, which allows distances between points on the earth to be measured from distance between representations of those points in a photograph of the earth. Two-dimensional maps can be produced from a single photograph and three-dimensional Digital Elevation Models can be derived from stereo-pairs of photographs. Errors are typically introduced into the final products due to the distortion created by representing a three-dimensional spherical object (i.e. the earth) on a two-dimensional photograph.
The second technology is generally known as remote sensing which uses ranging sensors to measure the distance from a sensor to the terrain or object. Interferometric Synthetic Aperture Radar (IFSAR) and Light Detection and Ranging (LIDAR), two known technologies, are examples of remote sensing technologies useful in creating terrain databases. The remote sensor is mounted in an airborne platform (i.e. an aircraft or on a satellite) and long strips of range data are collected as the platform traverses the earth. A Digital Elevation Model (DEM) is derived from recordings of the position of the airborne platform and the range to terrain and/or obstacles from the airborne platform. The raw DEM requires substantial post-processing (e.g. to strip out buildings or vegetation) to generate a useable DEM for most applications that require terrain and obstacle data. The final DEM may contain errors that are introduced when the raw DEM is recorded and may have additional errors introduced during the post-processing of the data. There are several types of errors that can be introduced into the database during post-processing of the survey data. Rugged terrain (i.e. spikes, cliffs, ravines, etc.) can “look” like a blunder to the software that searches for blunder errors. The result is that hilltops tend to get rounded-off while valleys tend to get filled-in. Both IFSAR and LIDAR are subject to the same distortions as aerial photography (tilt and height errors). So when parallel swaths are stitched together, terrain features in the overlapping areas of the survey may appear to be in different locations in different swaths. The software that stitches the swaths together must “correct” this problem. The most significant concern is that natural terrain features may be incorrectly identified as buildings or vegetation in the surface model and may be stripped-out of the bald-earth terrain model (or vegetation and buildings are misidentified as real terrain). While there may be a low probability of this occurring, the consequences of this type of error can be significant. Since the proprietary software that performs this function may not be available for examination, an independent means must be used to discover errors in the database.
It is therefore an object of the invention to provide a method for discovering errors in a terrain and/or obstacle database.
It is another object of the invention to discover errors in a terrain and/or obstacle database method using available or easily obtainable information.
A feature of the invention is using information from one source of terrain information to discover errors in another source of terrain information.
An advantage of the invention is that significant errors in a terrain/obstacle database can be easily discovered using readily available information.